Detection and classification of COVID-19 disease from X-ray images using convolutional neural networks and histogram of oriented gradients

Biomedical Signal Processing and Control, Journal Year: 2022, Volume and Issue: 74, P. 103530 - 103530

Published: Jan. 26, 2022

Language: Английский

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Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions

Cognitive Computation, Journal Year: 2023, Volume and Issue: 15(6), P. 1767 - 1812

Published: June 24, 2023

Abstract The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led many researchers to explore ways automate the process make it more objective facilitate needs healthcare industry. Artificial Intelligence (AI) machine learning (ML) emerged as most promising approaches CHA process. In this paper, we background delve into extensive research recently undertaken in domain provide a comprehensive survey state-of-the-art. particular, careful selection significant works published literature is reviewed elaborate range enabling technologies AI/ML techniques used for CHA, including conventional supervised unsupervised learning, deep reinforcement natural language processing, image processing techniques. Furthermore, an overview various means data acquisition benchmark datasets. Finally, discuss open issues challenges using AI ML along with some possible solutions. summary, paper presents tools, lists methods provides technological advancements, usage issues, domain. We hope first-of-its-kind will significantly contribute identifying gaps complex rapidly evolving interdisciplinary mental health field.

Language: Английский

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State-of-the-Art of Stress Prediction from Heart Rate Variability Using Artificial Intelligence

Cognitive Computation, Journal Year: 2023, Volume and Issue: 16(2), P. 455 - 481

Published: Oct. 12, 2023

Abstract Recent advancements in the manufacturing and commercialisation of miniaturised sensors low-cost wearables have enabled an effortless monitoring lifestyle by detecting analysing physiological signals. Heart rate variability (HRV) denotes time interval between consecutive heartbeats.The HRV signal, as detected devices, has been popularly used indicative measure to estimate level stress, depression, anxiety. For years, artificial intelligence (AI)-based learning systems known for their predictive capabilities, recent AI models with deep (DL) architectures successfully applied achieve unprecedented accuracy. In order determine effective methodologies collection, processing, prediction stress from data, this work presents depth analysis 43 studies reporting application various algorithms. The methods are summarised tables thoroughly evaluated ensure completeness findings reported results. To make comprehensive, a detailed review conducted on sensing technologies, pre-processing multi-modal employed models. This is followed critical examination how Machine Learning (ML) models, utilised predicting data. addition, reseults selected carefully analysed identify features that enable perform better. Finally, challenges using predict listed, along some possible mitigation strategies. aims highlight impact AI-based expected aid development more meticulous techniques.

Language: Английский

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Cross-Content Recommendation between Movie and Book using Machine Learning

Published: Oct. 13, 2021

Machine learning-driven recommendation systems are widely used in today's growing digital world. Existing movie and book recommender work using a collaborative approach, which can result lack of fresh diverse content reduced surprise factor. There is also no platform providing recommendations across different contents, such as for books from movies vice versa. In this paper, our main goal to introduce cross-content system based on the descriptions identifying similarities natural language processing machine learning algorithms. We processed combined dataset two types generated TF-IDF vector apply three algorithms: K-means clustering, hierarchical cosine similarity. being known research similar with ground truth labels, we applied subjective reasoning evaluate results system.

Language: Английский

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Mental Healthcare Chatbot Using Sequence-to-Sequence Learning and BiLSTM

Lecture notes in computer science, Journal Year: 2021, Volume and Issue: unknown, P. 378 - 387

Published: Jan. 1, 2021

Language: Английский

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Mixed Bangla-English Spoken Digit Classification Using Convolutional Neural Network

Communications in computer and information science, Journal Year: 2021, Volume and Issue: unknown, P. 371 - 383

Published: Jan. 1, 2021

Language: Английский

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iBUST: An intelligent behavioural trust model for securing industrial cyber-physical systems

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 238, P. 121676 - 121676

Published: Sept. 25, 2023

To meet the demand of world's largest population, smart manufacturing has accelerated adoption factories—where autonomous and cooperative instruments across all levels production logistics networks are integrated through a Cyber-Physical Production System (CPPS). However, these comprised various heterogeneous devices with varying computational power memory capabilities. As result, many secure communication protocols—that considerably high memory—can not be verbatim employed on networks, thereby, leaving them more vulnerable to security threats attacks over conventional networks. These can largely tackled by employing Trust Management Model (TMM) exploiting behavioural patterns nodes identify their trust class. In this context, ML-based models best suited due ability capture hidden in data, learning improving pattern detection accuracy time counteract tackle dynamic nature, which is absent most models. among existing solutions detecting attack patterns, computationally expensive, require long training time, large amount data—which seldom available. An aid association rule (ARL) paradigm, whose inexpensive do time. Therefore, paper proposes an ARL-based intelligent Behavioural (iBUST) for securing CPPS. For TMM, variant Frequency Pattern Growth (FP-Growth), called enhanced FP-Growth (EFP-Growth) algorithm developed altering internal data structures faster execution developing modified exponential decay function (MEDF) automatically calculate minimum supports adapting evolution characteristics. addition, new optimisation model finding optimum parameter values MEDF transmuting 1D quantitative feature into respective categorical facilitate model. Afterwards, class object identified Naïve Bayes classifier. This proposed evaluated evolution-supported experimental environment along other compared taking benchmark dataset consideration, where it outperforms its counterparts.

Language: Английский

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Speech Emotion Recognition: An Empirical Analysis of Machine Learning Algorithms Across Diverse Data Sets

Communications in computer and information science, Journal Year: 2024, Volume and Issue: unknown, P. 32 - 46

Published: Jan. 1, 2024

Language: Английский

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A Comparative Study of Classification Approaches for COVID-19 Prediction

Published: Feb. 27, 2021

The novel coronavirus (COVID-19), a highly infectious disease that first found at Wuhan Province of China in Dec 2019, spread worldwide some months and already become pandemic. Covid-19 has changed the world economic structure, people's religious, political, social life, public health daily life structure also made millions people jobless. only way to fight this epidemic is identify infected person as soon possible separate them from healthy person, so they can't infect anyone again. At present, RT-PCR currently used detect patients around world. But World Health Organization (WHO) said suffers low sensitivity specificity for early-stage cases. Recent research shown chest CT scan images play beneficial role identifying In study, we compared performances four classification algorithms, such Random Forest (RF), Support Vector Machine (SVM), Extra Trees (ET), Convolutional Neural Network (CNN) classifying COVID-19 cases proposed prediction model based on results. result shows our CNN outperformed other algorithms obtained an accuracy 98.0%.

Language: Английский

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Comparison of machine learning algorithms with regression analysis to predict the COVID-19 outbreak in Thailand

Indonesian Journal of Electrical Engineering and Computer Science, Journal Year: 2023, Volume and Issue: 31(1), P. 299 - 299

Published: May 17, 2023

Coronavirus disease (COVID-19) is a public health problem in Thailand. Currently, there are more than 5 million infected people and the rate has been increasing at some point. It therefore important to forecast number of new cases over short period time assist strategic planning for response COVID-19. The purpose this research paper was compare efficiency prediction COVID-19 Thailand using machine learning 8 models regression analysis method. Using 475-day dataset Thailand, results showed that predictive accuracy model (R2 score) from testing random forest (RF) model, which 99.06%, followed by K-nearest neighbor (KNN), XGBoost. And decision tree (DT) had precision 98.97, 98.67, 98.64, respectively. comparison obtained predicted real infections were tree, forest, XGBoost, effective predicting correctly 2-4 day period.

Language: Английский

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